Category Archives: AI-Driven Analytics

AI, Great Whites and Powerball

The human brain simply doesn’t have the capacity for making quadrillions of campaign decisions.

In our journey to develop the first cross-media planner that optimizes inventory and audiences across both proposals and in-flight campaigns, one of our learnings was that you simply cannot do this without machine intelligence. And without optimization supported by machine intelligence, it is impossible to maximize the revenue potential of your media business.

Why?

Consider optimizing five campaigns, each spanning four weeks and making use of four inventory pools. That’s 80 possibilities, right? Well, in the current way of thinking, that number may be in the right ballpark. Unfortunately, the current way of working pushes more effort onto the folks who steward the delivery of those campaigns. And, that’s where things start to get tricky.

You see, current work practices dictate that stewardship is just a fact of life. The emphasis is on taking business at any cost. The problem gets pushed down to traffic and delivery systems, et voila! We waste our inventory to make up deficiencies, and we churn our inventory and customer expectations. We wrack our brains to make stuff fit, and we usually end up doing this at the last, most stressful minute.

You see, the math actually is 80 choose 16 — i.e., how many combinations of 16 can I choose from those 80 possibilities to derive an optimum answer? Do the math. You’ll see 550 quadrillion or 550,000,000,000,000,000 possibilities that need to be reduced to a set of possible optimum answers. Certainly well beyond the capabilities of the human mind!

Now you see why we’ve used our own artificial intelligence stack to crack this problem. There really is no alternative if you want to do it right.

Interestingly, one of the keys to this solution lies in understanding the fundamental flaw in all of the traffic and sales systems and agency buying systems in the marketplace today. They simply focus on workflow, a workflow based upon priority placements. Their plotting/placement engines focus on getting the best overall delivery per order. Not a single one of them considers the destruction they wreak to other campaigns in the process, or the revenue potential they sacrifice to the business as a whole in doing so. So, they stuff placements in the schedule based upon some priority, push out others, and kick the debt down the road.

How did we ever allow our industry to accept this as best practice? Instead, we should optimize all orders, evaluate all proposals, and optimize all inventory pools for your entire sales ecosystem in real time. A total understanding of media economics is at the heart of this process, with a machine learning about your business in order to help ensure contracts are fulfilled.

Oh, and by the way, we did some math for fun; it’s just what we do! Did you know that your chances of being bitten by a great white shark and winning Powerball in the same day are — you almost guessed it — it’s actually about 110 quadrillion to one. That means your odds of finding one of those optimum answers are five times worse than of hitting it big in the lottery!

Using AI to Maximize Ad Sales Revenues

Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technologies — these are the new drivers of the modern media business. As the industry adapts to new and emerging content delivery technologies, data and analytics are the tools essential to extracting actionable intelligence and presenting it to the right people at the right time. AI and machine learning are taking analytics to a new level, improving and enriching the quality of data and, in turn, offering even more specific forecasts and more accurate insights into potential revenue opportunities.

National and local broadcast groups traditionally have considered monitoring of key performance indicators (KPIs) to be sufficient for performance analysis. They have relied heavily on one such KPI, revenue performance, which compares the networks or stations actual and projected revenue trend against their budgets. While executives can use this KPI to understand if they are on track to meet their financial commitments, they need more and better information if they are to understand how to improve revenues and address the factors contributing to budget shortfalls.

Only AI can effectively examine the tens of millions of data points that reflect sales performance, market trends, and business cycles across many different categories, such as lines of business (local, national, digital, etc.), products, sales offices, and even individual advertisers. In addition to monitoring sales operations and surfacing new alerts, opportunities, and risks to relevant stakeholders, AI learns as market conditions change and as the business grows and evolves. Using machine learning (ML), AI-driven analytics systems use algorithms to parse and learn from data, and then use that understanding to make forecasts or predictions.Product Data Point Analysis

AI learns what “normal” business cycles look like, then continuously monitors for new risks and opportunities.

While many industries are applying AI and ML to the massive collections of data associated with their operational systems, the media industry is just beginning to leverage these powerful tools to support business strategy and success. In fact, Decentrix is the first media technology company to leverage sophisticated media-centric AI and ML to enhance the revenue opportunities of media, entertainment, telecommunications, and advertising companies in the cross-media marketplace. Our BIAnalytix platform uses AI and ML to expose critical data within operational systems and to deliver insights that yield maximized inventory pricing, enhanced audience values, and optimized campaigns across all properties and platforms, across linear and digital business models, and across OTT and ATSC 3.0.

Both national and local sales enterprises may be able to produce some of these insights without the benefit of AI or ML, but the sheer size and scope of data prevents them from doing so quickly or comprehensively. Implementation of media-centric, AI-driven analytics allows a media group to automate and accelerate analysis of all data and to extract the insights essential to capitalizing on opportunity in a complex and competitive marketplace.